TCGAbiolinks has provided a few functions to download and prepare data from GDC for analysis. This section starts by explaning the different downloads methods and the SummarizedExperiment object, which is the default data structure used in TCGAbiolinks, followed by some examples.

Downloading and preparing data for analysis

Data download: Methods differences

There are two methods to download GDC data using TCGAbiolinks:

  • client: this method creates a MANIFEST file and download the data using GDC Data Transfer Tool this method is more reliable but it might be slower compared to the api method.
  • api: this methods used the GDC Application Programming Interface (API) to downlaod the data. This will create a MANIFEST file and the data downloaded will be compressed into a tar.gz file. If the size and the number of the files are too big this tar.gz will be too big whicih might have a high probability of download failure. To solve that we created the chunks.per.download argument which will split the files into small chunks, for example, if chunks.per.download is equal to 10 we will download only 10 files inside each tar.gz.
Data prepared: SummarizedExperiment object

A SummarizedExperiment object has three main matrices that can be accessed using the SummarizedExperiment package):

  • Sample matrix information is accessed via colData(data): stores sample information. TCGAbiolinks will add indexed clinical data and subtype information from marker TCGA papers.
  • Assay matrix information is accessed via assay(data): stores molecular data
  • Feature matrix information (gene information) is accessed via rowRanges(data): stores metadata about the features, including their genomic ranges

Search and download data from legacy database using GDC api method

In this example we will download gene expression data from legacy database (data aligned against genome of reference hg19) using GDC api method and we will show object data and metadata.

query <- GDCquery(project = "TCGA-GBM",
                           data.category = "Gene expression",
                           data.type = "Gene expression quantification",
                           platform = "Illumina HiSeq", 
                           file.type  = "normalized_results",
                           experimental.strategy = "RNA-Seq",
                           barcode = c("TCGA-14-0736-02A-01R-2005-01", "TCGA-06-0211-02A-02R-2005-01"),
                           legacy = TRUE)
GDCdownload(query, method = "api", chunks.per.download = 10)
data <- GDCprepare(query)
# Gene expression aligned against hg19.
datatable(as.data.frame(colData(data)), 
              options = list(scrollX = TRUE, keys = TRUE, pageLength = 5), 
              rownames = FALSE)
# Only first 100 to make render faster
datatable(assay(data)[1:100,], 
              options = list(scrollX = TRUE, keys = TRUE, pageLength = 5), 
              rownames = TRUE)
rowRanges(data)
## GRanges object with 21022 ranges and 3 metadata columns:
##                seqnames                 ranges strand |      gene_id
##                   <Rle>              <IRanges>  <Rle> |  <character>
##           A1BG    chr19   [58856544, 58864865]      - |         A1BG
##            A2M    chr12   [ 9220260,  9268825]      - |          A2M
##           NAT1     chr8   [18027986, 18081198]      + |         NAT1
##           NAT2     chr8   [18248755, 18258728]      + |         NAT2
##   RP11-986E7.7    chr14   [95058395, 95090983]      + | RP11-986E7.7
##            ...      ...                    ...    ... .          ...
##            FTX     chrX [ 73183790,  73513409]      - |          FTX
##   TMED7-TICAM2     chr5 [114914339, 114961858]      - | TMED7-TICAM2
##          TMED7     chr5 [114949205, 114968689]      - |        TMED7
##         TICAM2     chr5 [114914339, 114961876]      - |       TICAM2
##    SLC25A5-AS1     chrX [118599997, 118603061]      - |  SLC25A5-AS1
##                entrezgene ensembl_gene_id
##                 <numeric>     <character>
##           A1BG          1 ENSG00000121410
##            A2M          2 ENSG00000175899
##           NAT1          9 ENSG00000171428
##           NAT2         10 ENSG00000156006
##   RP11-986E7.7         12 ENSG00000273259
##            ...        ...             ...
##            FTX  100302692 ENSG00000230590
##   TMED7-TICAM2  100302736 ENSG00000251201
##          TMED7  100302736 ENSG00000134970
##         TICAM2  100302736 ENSG00000243414
##    SLC25A5-AS1  100303728 ENSG00000224281
##   -------
##   seqinfo: 24 sequences from an unspecified genome; no seqlengths

Search and download data for two samples from database

In this example we will download gene expression quantification from harmonized database (data aligned against genome of reference hg38) using GDC Data Transfer Tool. Also, it shows the object data and metadata.

# Gene expression aligned against hg38
query <- GDCquery(project = "TCGA-GBM",
                  data.category = "Transcriptome Profiling",
                  data.type = "Gene Expression Quantification", 
                  workflow.type = "HTSeq - Counts",
                  barcode = c("TCGA-14-0736-02A-01R-2005-01", "TCGA-06-0211-02A-02R-2005-01"))
tryCatch(GDCdownload(query, method = "client"),error = function(e)GDCdownload(query))
data <- GDCprepare(query)